对汽车雷达对象分类神经网络的网格搜索和遗传算法优化
Atila Gabriel Ham1, Corina Nafornita1, Vladimir Cristian Vesa2
1Communications Department, Politehnica University of Timisoara, 300223 Timisoara, Romania.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
基因算法优化显著提高了汽车雷达对象分类准确性,用于汽车,行人和骑自行车的人. 这种方法增强了特征集和模型深度,达到97%以上的准确性,优于网格搜索方法.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 汽车工程 汽车工程
背景情况:
- 对象分类对于汽车雷达系统至关重要.
- 传统的方法难以应对复杂的交通场景.
- 神经网络为改进基于雷达的物体检测提供了潜力.
研究的目的:
- 用汽车雷达数据评估两种神经网络方法来对物体进行分类.
- 为了比较网格搜索与基因算法 (GA) 对超参数优化的有效性.
- 调查特征集扩展对分类性能的影响.
主要方法:
- 开发了两个神经网络模型来分类汽车,行人和骑自行车的人.
- 应用网格搜索和GA用于超参数优化.
- 使用运动相关性,运动描述器,物体尺寸,SNR,RCS和卡尔曼波器描述器作为特征.
- 使用主要组件分析 (PCA) 和夏普利添加式解释 (SHAP) 进行特征分析.
主要成果:
- 网格搜索优化在一个紧的模型和基本功能的情况下产生了~90%的准确性.
- 使用更深入的模型和扩展的功能集,GA优化实现了~97.4%的准确性.
- 扩展的特征,包括对象尺寸和RCS,显著提高了辨别力.
- 并行GA使高效的超参数空间探索成为可能.
结论:
- GA超参数优化与扩展的功能集相结合,大大提高了汽车雷达对象分类性能.
- 拟议的方法在城市交通中显示出高精度,并具有更广泛应用的潜力.
- 进一步开发可以将强度扩展到各种环境和运动模式.
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